Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems

This research explores the application of archived data from Automated Data Collection Systems (ADCS) to transport planning with a focus on bus passenger travel behavior, including Origin-Destination (OD) inference, using London as a case study. It demonstrates the feasibility and ease of applying trip-chaining to infer bus passenger OD from smart card transactions and Automatic Vehicle Location (AVL) data and is the first known attempt to validate the results by comparing them with manual passenger survey data. With the inferred OD matrices, the variations of weekday and weekend bus route OD patterns are examined for planning purposes. Moreover, based on the inferred OD matrices and the AVL data, alighting times for bus passengers also can be estimated. Bus journey stages, therefore, can easily be linked. By comparing the interchange time and the connecting bus route’s headway, it provides a way to evaluate bus connections. Journal of Public Transportation, Vol. 14, No. 4, 2011 132 Background and Purpose London has one of the largest bus networks in the world, with more than 6 million passengers transported on its 700 routes daily. A recent report states that “Bus usage is growing at its fastest rate since 1946. More than two billion passenger trips were made on London’s fleet of more than 8,000 buses in the year to March 2009. The number of operated kilometers has also risen to 478 million, the highest since 1957” (Mayor of London, 2009). Every five to seven years, a Bus passenger Origin and Destination Survey (BODS) is conducted by Transport for London (TfL) for each bus route. This survey provides detailed information about passenger travel patterns, including the number of people boarding and alighting at each stop, the purpose of travel, the boarding and alighting locations for each journey, and how passengers get to the boarding stop and from the alighting stop to their final destination. Expansion factors are used to account for non-returned survey cards and non-surveyed bus trips. An automated database (BODS database) stores survey results, including boardings, alightings, and loads at each stop (or stop zone) for each route. BODS is one of the primary data systems used by the Bus Network Development Unit at TfL. A limitation of this type of survey is that it records passenger travel for only one day per route. Recognizing the substantial network growth and the dynamics of demand, supplementary data from other sources also are needed for network planning. Moreover, although surveyed passengers are asked for their ultimate origin and destination in addition to their travel on the route itself, this information is not generally transferred from the paper surveys into the BODS database and therefore is not readily available to network planners. In addition to BODS survey data, London bus planners also get timely route-level passenger ridership data from Electronic Ticketing Machine (ETM) transactions, which are downloaded daily from each bus at the garage. One drawback of this data collection method is that this data source only records aggregate ridership for each bus trip, while detailed information such as boarding and alighting locations for each passenger cannot be obtained directly. The Oyster smart card system was launched in London by TfL in December 2003 as a new ticketing medium (Transport for London). It is now accepted on the Underground, buses, the Docklands Light Rail (DLR), Tramlink, and National Rail stations. Though the full potential of this data source has not yet been realized by London bus network planners, Oyster data are readily available, provide large sample sizes, and potentially offer a full network perspective rather than strictly a